System Development & Application
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 6
1790-1796

Multi-perspective behavior analysis of fusion processes based on deep learning: predictive business process monitoring

Yuan Yongwang
Fang Xianwen
Lu Ke
School of Mathematics & Big Data, Anhui University of Science & Technology, Huainan Anhui 232001, China
DOI: 10.19734/j.issn.1001-3695.2023.10.0526

Abstract

Predictive business process monitoring(PBPM) represents a vital research field within BPM that aims to accurately predict future behavioral events. At present, deep learning methods are widely used in PBPM research. However, most of these methods consider only a single event-control flow perspective and do not fuse the attribute-data flow perspective for process prediction. To address this issue, this paper proposed a method called the fusion multi-perspective(FMP) framework based on a two-layer BERT neural network. Firstly, the first layer of BERT was used to learn attribute-data flow information. Subsequently, the second layer of BERT learnt event-behavior control flow information. Finally, the FMP framework combined data flow and control flow to achieve multi-perspective process prediction. Experimental results on real event logs demonstrate that, compared to other research methods, the FPM framework yields higher accuracy in predicting the next event activity. This validates that the FPM framework, which merges multi-perspective views of processes, enables a more comprehensive and in-depth analysis of complex process behaviors while enhancing predictive performance.

Foundation Support

国家自然科学基金资助项目(61572035)
安徽省重点研究与开发计划资助项目(2022a05020005)
安徽省自然科学基金资助项目(水科学联合基金2308085US11)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.10.0526
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 6
Section: System Development & Application
Pages: 1790-1796
Serial Number: 1001-3695(2024)06-027-1790-07

Publish History

[2024-02-01] Accepted Paper
[2024-06-05] Printed Article

Cite This Article

袁永旺, 方贤文, 卢可. 基于深度学习的融合流程多视角行为分析:预测业务流程监控 [J]. 计算机应用研究, 2024, 41 (6): 1790-1796. (Yuan Yongwang, Fang Xianwen, Lu Ke. Multi-perspective behavior analysis of fusion processes based on deep learning: predictive business process monitoring [J]. Application Research of Computers, 2024, 41 (6): 1790-1796. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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